{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "# LSTM 股票时间序列预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>30.57</td>\n",
       "      <td>30.57</td>\n",
       "      <td>28.63</td>\n",
       "      <td>28.78</td>\n",
       "      <td>70997200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>28.41</td>\n",
       "      <td>29.54</td>\n",
       "      <td>28.23</td>\n",
       "      <td>29.23</td>\n",
       "      <td>87498504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>29.03</td>\n",
       "      <td>29.39</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.26</td>\n",
       "      <td>48012112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.25</td>\n",
       "      <td>27.73</td>\n",
       "      <td>28.50</td>\n",
       "      <td>23647604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.18</td>\n",
       "      <td>27.63</td>\n",
       "      <td>28.67</td>\n",
       "      <td>98239664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close    volume\n",
       "0  2016-01-04  30.57  30.57  28.63  28.78  70997200\n",
       "1  2016-01-05  28.41  29.54  28.23  29.23  87498504\n",
       "2  2016-01-06  29.03  29.39  28.73  29.26  48012112\n",
       "3  2016-01-07  28.73  29.25  27.73  28.50  23647604\n",
       "4  2016-01-08  28.73  29.18  27.63  28.67  98239664"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv(\"zgpa_train.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    28.78\n",
       "1    29.23\n",
       "2    29.26\n",
       "3    28.50\n",
       "4    28.67\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = data.loc[:,'close']\n",
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.383273\n",
      "1      0.389266\n",
      "2      0.389666\n",
      "3      0.379545\n",
      "4      0.381808\n",
      "         ...   \n",
      "726    0.751099\n",
      "727    0.750566\n",
      "728    0.738447\n",
      "729    0.733120\n",
      "730    0.722466\n",
      "Name: close, Length: 731, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#归一化处理\n",
    "price_norm = price/max(price)\n",
    "print(price_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+JwD5gzROHUj3asSeQZ/XH8Dt69uodduhNr7yz/V87p61ALi83cX/2U5NNcroosBLRCTBdHr8pPXTTgL61kQdaHKR6XRw8vRCTp5WEDneu4+Xy+OnqcNLSbaTj59aEXmtIGP4gVe41iy6xqvd7eOUnzzHx//yVp/z//BiMMgLT5V2RrXLUHG9jDYKvEREEsjO2nbcvr6d68Oy01J61FZVNroiXeqXTM2PHJ+Ylx557Axda1tNG8unF/Jf58+JvHZEGa/0vrVmD649QH27h9d3NfTo79Xi8vL4xmDD1oC1vLitlvp2DxnRU43KeMkoosBLRCRBVDa6OPeXLwEMEng5eqxq3N/oimSSFk3JA6A8N42Kou4WEeHVjBDsgB+dDRtoc+zBpKcEa82ia7y2Ru0hGV3wv6+xA48/wIlT8nB5/PzxpeAU6dlzSwAV14+UnzyxhYfXVcV7GGOC4/CniIjIePDLZ7ZHHvfXTgKCNVHVLd1bBgVs97RiRqqDBz57CtOLenai/+gpU9lc1cJzW2vZVdfe47W8IyiuN8aQ02vKc2dN93X31LdTkBmc9gw3eJ1dks3b+5vZXtPOWXOKWRFqYZGT7sDjDwzYu0yOjT+9tBuASxdNjPNIRj9lvEREEsC2Q2089HZ3RmKwjFfvTaUzogrpl0wt6DN9WJTl5OdXnADAnNJsAC5cMAEAp+PIgp3glGdwHNZatte2sXx6MNjaU993S6NZpcFgsLHDQ2GmM/J6fqjGrMnVsxGsHDs7a9sPf5JEKPASEUkAm6paACjODgYlAxXX52WkUtXc2eNYdOuIgeRnpvLsV87ge++dD8BvrzqRzd+/4IjHm5PenfGqb/fQ7PJGpg8rG6MCr3DGKxTwARRldQeGeaGeYM0utZSIhRe21kamrweypbqVipse4/Wd9SM0qtFNgZeISAJwhfZPnFqQAYAZ4Lz+arKia7YGM7MkO7Ka0NGr1mu4sp0pkRqvHbXB+q7jynLI6ZWRa+jwkJJsmBZVc1YYFXjlZijwiqW39jYe9pzXdzUA8PQ7NbEezpigwEtEJAF0eYKBV3loNeJAU2/9rULMOooA6khFZ7zCU1mzSrLJy0ilOTT2O17dwx9f2kVuemokkwf0mGrMSw9+P82aajzmbnt5V6TfGxDZLWA49jV04PIk1uIHBV4iIgnAFQq8rj55CgBLpxb0e1503615ZTnA0DNex1J0jdf2mjay0xyU5jjJz0ihocNDl9fPLU9sBYJ7Q0YXzhdFBWH5maGM1zC64MvQ/PjxrT2e56b3v5Aiuv1Hb2f+7EWuuu3NYzqu0U6Bl4hIAuj0+klNTuLk6YXsveVi5pXn9Hte9FTjKTMKgeDejiMt3EEfghmvmSVZGGPIzUjllR31zP3Ok8ws6bm6sqIwI/LesO6MlwKvY21ZqKHu/NB/S4EBAixfIHjc9Jrf9oX281x/oCVGIxydFHiJiCSAYDuFw//Ijw68whmvujZ3zMY1kLSUZNyhX8y1bW7Kc4NTpHlRWRV/6Bf6e04oB+BXV57I4il5HFeWHXWdJFIdSZpqjAG3188Zs4t57Aun8/FTK+j09B+gh/vChf++wlxRAf3tr+yO3UBHGQVeIiIJwOXx9WgLMZDw1BzAxQvLuGLJJL5wzqxYDq1fTkcSHl+AQMDS7PJGiuSj+4Jtq2njqmVT+O1VJwKwaHIeD95wao/v0xhDfkaKMl4x0O72kR3KLqanJNPlDfR7Xmtnd+ZyyQ+fYXWoIN/l7g68fvTYlj4bso9XCrxERBJApzcQWXE4mPyoGq+0lGR+dsUJTA6thBxJzlB2rsvnp9nlIT8UcIU30A6bkJN22GvlZ6Sydn9Tj70fAZ7cdIjP/H31oDVIMrB2ty8yrZuWkozHH4hMH0YL3/fXdzXQ0OHh3lWVAHT0Kqp/bEM1gcD4/7tQ4CUikgA6Pb4hdW5PSQ7+WpgSh2ArWrjxan2bh4DtDgg9vX6xl+Y4+7y3t8+fPYsdte08sbG6x/HP3r2GpzbXcKCpc4B3ymDau3xkpXVnvAC6fP0EXr0WNqQ6gv+NhTNeP33/QgC+/sAGXtpeF7PxjhbaMkhEJAF0ev1DXu7/7FfOoCjr8AFNLDlDv5xr2oLbF+WFAq/2XtNRE3IPn/E6c04xQJ+O/LNLstlW08abuxviktUbywIBS4fHH1nxGm7I2+nx92k/0tprr8wDTcEGuOG/y0kF6eRnpNDk8lLZ5GK8U8ZLRCQBdHr8A24T1NvMUL+seAoHXodC+0aGpxo/uHRyj/PCKy8Hk5GSjDFEVkmGlYSyZav2HL4JqPQUniaMrvGC/lfA9s54hXceCPfvynI6ePUbZwPQ1NF9rtvn5z/rD467qWBlvEREEoDL46cwzlms4XCGfpHXtPbMeC2anMfeWy5m44EWyvLShrQXZFKSITPVQbu7Z1AQbtC6Q3sNDls4W9V7qrGzv8Arqrbu/HmlvLCtFn8oYwbBvUAznQ7yMlJo6OheQfv9/7zDPSv3U5abxtKK/vvOjUXKeImIJIAu79AzXqNB2gAZr7DjJ+UOazo005lMu7tn5iUcEOyuax93WZVYC2cPw9OK6anBv6/+Wkp0RAW8y6cX4vVbWjq9uELBW2ZoL9DCzFQa2rvbfvxn/UGAPosixjoFXiIiCcA1jKnG0SCc8TrUGg68jm7qM8vp6BEAQHebg9YuHw0d6vM1VNZabnpwI0CPVY3QN+NlrcXl8fH5s2ey95aLI+e7PL4eGS+Awiwn9e1urLW8sqOue5P0tvH1d6PAS0QkAXR6/UNqJzFahGu8alvdGAM5A2xHM1RZTkePwnxrLa2dXuZOCDZb3VPfcVTXTyS1bW7W7GsC+k41Xnnbmz3us9sXIGC7g6twEX6X1x/p2xVe9FGUlcrmg61M++bjXPPnVZH9N+va+zbw3VTVwrm/fImnNx+KxbcYUwq8REQSQNcYDbwOtXaRk5ZCcpI5zDsGl9kr8HL7Anj8gci2Nxt6bVvz4rZafvf8jqP6zPFqb1SQmhkKqKKb1j68riryuHdwlRHOjHkCdHh8pDqSIi1MCjOdPf6OVswoJMvpoL6fwOvN3Q3srG3nC/e9PeamiRV4iYiMc15/AK/fjq2pxlDR/KGWrj71XUciy+lgzb4mbnpgA9C90m52aTbTizJ5bWd9j/M/9pe3+PnT2xOioedw7WsIrkr82gVzItszzSrJ4tsXH0dBZipPba6JnOuKTCcG/z7Dwb/L48Pl9pMZ9Y+B8rz0Hp8zKT+doqxU6tv7TjWGt7Hq8gaoah5bfdgUeImIjHNtvQqhx4Jw53qPP3BMWluEv/f73gp2TQ8XbOekp3DqzCLe3N3QZy9B6O4jJt32NnTgSDJ85ozpmNDO10lJhk+dPp355Tk92keEA6/MSBF+dy1Yh8cXOQ7w0VOmct91y5kYCsAm5WdQmOWkoZ+MV/T+oeFpz7FCgZeIyDgX/sVVmBXf3lzDEZ5qhL4rGo9EhrM7s+LxBSLNVLPTHMwqzcLl8dPUz0ba4exOLHR5/ZFVm6PNJ//6Fif/+Nl+X9vX4GJyQQaO5L4hRHQt3aMbDnLBr14GugOuSNsJj5/q5q4eLU4ynQ6WTy+MnDs5PyNS99W7cW5tm5uFk3JJdSSx+WDrUX63I0uBl4jIOBdesRfvbvTDEd2f62hXNEL3CkaAqubOyPRVcZYzcv2mflY27o9R4OXzBzjxB89w6v8+H5PrH63nttZS09o30wRwsKWT8rz+dwzIdDo40OTi9ld2c+M9b3cfj9SChaca/bxT3cq8spw+15ga2kWgONvJ+fMm0NLp5TfPBevtrLUEApbati4m5KRRnps25qYax07eWUREjki4N9JYynilpXTnBY7FVGP01NT+RlekMWtJjpNmVzD71RT66vZ1t0TY1xib1Y4vbKvrt9noWFDf7mbxlPx+X8tyOujyBvjRY1t6HM/olfHaVddOS6eX+eV9A6+fXXECT2yqZnZpFnMmZPO3N/ex4UAzAH9+dQ8/emwLTkcSy6YV0O72cXCMBV7KeImIjGP/fruKz92zFoCCzLETePXMeB39VOPcUBE4wP6GDmpau0hOMhRlOsnPDF6/MZTxim7iOVDW52i9vb+7Lik60Btt+tsCqL7NM2D2dKA6wt41XqtDdVnz+gm8CjJTufrkqZH6sbml2eyoCe4u8MtntgPBVakl2WmU56VT3Tw6p2sHosBLRGQc+9PLuyOPC+K8/+JwpCR3t4/IOwaB100XzuWxL5xGbnoK71S3UtPqpiTbSVKSiQSk4Rqv6PYFbTHomv7ohoPc+uKuqM/wDXJ2fIWzgRAMwj5y+0o6vf4BA6/MAQKvcMYr3Gh1fWUzxhDpozaYWaVZNHR4WLu/KVKsD8EVqeW5adS0deH1B4Dg1HA4mzlaKfASERnHCqOyXP0VQ49W4WwHQP4xyNQ5HcnML89l0eQ81u5rpqa1i5KcYJ1SuMYrnPEKF7ynOpJ61IYdK9975J0ez3tv3h1vvlAQA/RYcLCusplXQ203igaYts6KWsQQnf0KB14pyUmkJBvcvgDTizJ79P8ayKzSYHB2+a2v9zh+1pxiyvPSsRae3BRspHrGz17gtFFaNxc2dv4vFBGRYbOM/T5UJ0zKO2bXWjwln+21bWypbmVCTjBrk5aSTHpKMs2hICO8afaJk/OO2T6BVc2dWGux1uL1B8hJc/C998wDRkfG65+rK3lpex0Q3EIpbF+DK9LVf39j90KDouzBM14pyYbV3z43cjw6wArXec0rzx3S2BZPyYs8Ls9N42MrKvjiObNIS0lmTihj9tX710dWPnr9o/u/eRXXi4iMMx1uH+kpyTy6sZrXdjYAMLUwI86jOnKTC47d2M85roTfPr+D+nYPZbndDTsLMlNp7PDy8LoqfvbUNibmpTMxL51VexuP+jN31bVzzi9e4tzjSrl0UTktnV6+9555zA2t6IvFdOZwff1fwcayW3/47h4tLq6/aw0Au398EbtCASlAUebgNV7pKcmRaUWgx84D6anJtHb5+l3R2J/stO6p5ns+vZyKoszI8xOn5PPV82bzi2e28+w7Nf29fdSJaeBljMkDbgcWABb4BLAN+AdQAewFPmitHVvdz0RERqlOj5/T/vd5SnPS2HqoDYBrlk/l5lB2ZSw5eVoBM0uyjuk1F0zM5W+fWMabexq5atnkyPH8zBQaO9x88b51QDCAyElP6dEM9EjtqQtmjJ7dUsOzW4LBwazS7EiQ0uaOb8YrOvA75SfPRVZ3RqtscrEzKvAaaIVs+HsabFo7mP1y91tYP5A/XbOExzdW9/sPiBUzi/jFM9v55+rKyLG2Lm+PgG00iXXG69fAk9baDxhjUoEM4FvAc9baW4wxNwE3Ad+I8ThERBLCbS/vpsnl7fHL0+Xxj6n6rrB/fOaUmFx3xcwiVsws6nGsNDuNg1GZnnnlOeSkOWhz+wgELEm99oqsau7khrvW8MVzZ3H23NJBP+9AU99eYHMmZONyBwvF4z3VuLe+e3z9BV0Amw+2sr/RxakzC7lm+dQ+2/uERU81AvzlYyex+WDPfTDDmbChZrwALpg/gQvmT+j3tfB04+u7GiLHqlu6Rm3gFbP/E40xOcAZwJ8BrLUea20zcClwZ+i0O4HLYjUGEZFEsq6ymf/37HbOm1fKDy6dz2+uOpEkA+9fPDHeQxv1Juans6U62AH98sUT+cGl88lJT8FaaPf0DYze2NXA+gMtfOKvqw+7SfPefpqwFmU5yU4LBintcZ5q3NMQzMh9aOlkXvivs1hWUdDnnI1VLTS5PEwtzOTdC8oGvFY48HIkBcOLd80t4cazZ/U4JyM1mZJsJ8UD1IkNV5bTwfETg/Vi1585A2BU9/aKZcZrOlAH/MUYcwKwBvgiUGqtrQaw1lYbY0piOAYRkYSxL/QL9BvvnhuZonvvCeXxHNKYMSm/O4Nz0YIystNSyAllTFo7vZHHYXvruxurtnb6uHvVPrKdDq45paLPtaOL0oHIxtBZocAr3hmvcO3W9947n/TUZP72yWWs3NPIz5/axsaqYLbq0Q0Haejw9Fgl2x9HKDM4WLPe02YW4fYFBnz9SDx4wwo63D46vX7++NKumG71dLRimXt2AIuBP1hrTwQ6CE4rDokx5jpjzGpjzOq6urpYjVFEZNwIr+oKZ1Jk6Cbld9cOTSsOFm/npAfvY38tJcJZIggGVj99chvfeXhzv9eubHRxwfxStvzg3QDc/J75QLC1QlpKUp99CI/Errp2qluGn+UJBCwPr6ti8ZS8SHPTtJRkzpxdHOlvdlJFPpWNnVh7+O2bphZm8LUL5vCHjywZ8Jwvnzebmy6cO+yxDiYlOYm8jFQm5KRRlJXK+lCn+9EoloHXAeCAtXZl6Pm/CAZiNcaYMoDQ19r+3mytvc1au9Rau7S4uDiGwxQRGR/CmRMFXsM3MapmaXpo1Vwk4xU1FWitZVNVC49tqI4EJi9s6/411tDet9N9Y0ew03t6ajJ7b7mYD57UXdSfk5ZCZT81YMPhD1jO+cVLvOe3rw37vesPNLO3wcVHlk/t81q4ce0Zs7p/Bx9u2yljDJ9718we93MkGWNYNDmPdZXNcfn8oYhZ4GWtPQRUGmPmhA6dA7wDPAJcGzp2LfBwrMYgIpJI2rt8JCeZSJ8kGbrwarmrlk2ONG/NSe+eagTw+gOc9D/PcslvXwXgXXOClTL/XlcVuc7b+5t7XDcQsDS5PANmii5dVM4Tmw5F6suOxKo9wZYX9f0EfYcTnpJbOKlvT613h4rZoxciHIsNy2PtxCn57K7r6DcIHg1ivczl88DdxpgNwCLgx8AtwHnGmB3AeaHnIiJylNq6vGQ5HT26vsvQ5GWk8srX38X/XHZ85Fh3xiuYSWxo91Af2sfxWxfN5dsXH0eW08Huug7Ct3xnXXuP67Z2eQnYgbvvf/r06Vjbc0XecIVbVGQ5HazZ18h/P7SRnbVtQ3pvVagIvb9VihceX8bqb5/LkqndG2KPhf0+z54bDIifCHWzH21imo+21q4Dlvbz0jmx/FwRkUTU5vZpmvEo9G7U2l3jFcx4hbfP+fWVi7h0UXClaEVRBpuqWplXlkNloyuymu5QSxcTctMi7RkG2ui7JCeN4mxnn5YLw7E2tOF2h8fHTQ9sZEdtOyXZaXzx3MPvg3igqZOCzNQBt+7pvSfjWAi85k7IZkZxJk9squ53CjXexl5jFxER6Vdbl6/H/nhydML3Mlzj1RTay7EkOy1yztwJwV5UFYWZlOelc7C5i1d31LP8J8/xg/+8E9n/cbD9JueV5fDy9vrIRs/D4fb52VzVSk6aA2u7tzuK3mNxMFXNnUOqxwpPX4+FwMsYw4oZRayvbCEQGH3bBynwEhEZJ9q7fH3aHsiRcyQnkeV00BLJeAW/Rgcfs0JtO7KcjlDg1RlZUXfXm/u46819wfcMUht1wuQ86tvd3PTAxmGP8aVtdXj8AS46vmdvreahBl5NriEFXg/feCr/fdFxPbYBGs0WTMyh3e1jX+PoayuhwEtEZJxoc3sjvaHk2MhJc0TaSTS6wtmr7uA23ATUmZJEWW4a1S2dkR5fHn+Ah94OFt4PVpT+2TNnUJTlZFvN8Avsf/H0dmaWZPHhk6dEjpXnptE4QAf63g61dFGWl3bY82aXZvPpM6YPe3zxsiDUUDXch2w0UeAlIjJOtHepxutYy0lP6TPVmJfeHURdvLCMT502jS+eM4vyvHSaXF42H+wbQEUHa72lpyZz3rySHptTD9Xehg7OmVvCvLIcPnPGdP587VJmT8jukfFqaHdz5s9e4NENB3u8t8Pto8Pj7zF1Ol7MLs0mOcmw/dDQFhmMJAVeIiLjRJsCr2MuJy24UXaX10+Ty0O200Gqo/tXp9ORzLcvmUdhljMy7fhOdSvZvWrtDld7V5abTn27B7fPP+SxdXn9uH0BctJTcCQn8c2LjuOc40opyEiN1JYB7KnvYF+DixvveRtPVMf4cPuJY7V1z2iSkhzMQB5tj7RYUOAlIjIOWGtDxfWq8TqWctIdrNzTyNzvPMm/1hwgb5DM1fIZhZHHZ83t3g3vgc+uOGyLj7LcYNZpOFmvcO1ZbnrPMeVlpNIcNdUY3QB2X1TH/XDgVXSYpqhj1eT8DCpV4yUiIrHQ2unD4w+M21+i8RKd3Wrr8g1aJJ+TlkJyaK/CT542LXI8ug/WQMJ9tA42Dz/wyuvVqqIgM4V2ty+S3Yre8mhnbXefsbq28ZvxAphckE5l0+jbLFs5aRGRcaC6NfgLpiw3Plu1jFfrK3sWZ4eLtgfy9JfPoNPjpzRneHVT4YzXgSYXUDj4ySGDZbwguLKxJCctch4E201cGHo87gOv/Azq2tx0ef2jajWmMl4iIuNAdWiKakLu+CuUjqcbz55JWkpSpH7r7KgpxP7MKM5iwcTcARumDmRqYSbZaQ7W7Gsa8nvC04nRxf4AhaF2F3Xtbh5Zf5CbHwlu3j0hJ43tNd3F5nXtHpIMFGaOz8BrSmgbqPC2SKOFMl4iIuNAdWiKqkyB1zF11bIpXLVsCqv2NPLb53dwatS+hYNxJAfzGuceVzqk85OTDMunF/Larvohj22gjFdJKNtW2+bmC/e+DUBKsmFpRT4r9zRircUYQ12bm4LM1Mj06HgzvzyYnVx/oJk5Ew7fxX+kKOMlIjIObDvUSpKBknE6bRRvy6YV8PdPnjysKat3fnABf/zI4iGff/K0AiobO4e82fWAgVfov4G61u7reP2Wd80poa7NHWl3Udfm7rMl0HgyvSiTnDQHb+8fehZxJCjwEhEZ4+5btZ8739hHwHZnWiT+MlIdw/r7CNfn9Q68/AHL71/YSUuvpqgtLg/G0KeFSElOMJiqae1ZqL9iZrB2LByI1Le7x219F0BSkmHRlHyeeaeG+1dX4h8l2wfp/1ARkTEsELD8/sWdAFw3hjqLS1/hrYga2z20u7tXIq7e28jPntrGtx7quaVQS6eXnLQUknpNFTodyeRnpFDT1jPwCme3wlsf1bWN78AL4L/On03Awtf+tYE3djXEeziAAi8RkTHL6w/w2q56Khs7ueXy4/nWRcfFe0hyFMKtQP7vld0suPmpSOsHT2jz7A1Vzfzjrf2c/fMXae3ycqCpc8CavpLsNGpa3TiigrKU0N6TzS4v1lrq2t0Uj+OpRoCFk/J4+stnALCtZnR0sVdxvYjIGPXDR9/hb28EN2E+riwnzqORoxXOeL2wrQ6AvfUdzCzJimSoKhs7+UZoI+1NVS3srGsfsL1FSY6TvfUd+ELTa5Pyg9OYeRkpNLs8tIX6fI33jBcEV3nmZaSwq6798CePAGW8RETGqPtXH4g8nhlqdyBjV15GKtEN7p0pwV/R0fsuhv3+hZ3sa3Axs7j/v/cZxVnsCGXMvnjOLB77/Omhz0ihudM77nt4RTPGMLM4q0fz2HhS4CUiMgb5A7ZHsXDmYfYClNEvOcmQH9UZ3xuaYmzq8PY597WdwXqlGQME3O9eMCHyeOGkXHJDfcXy0lNpdnm6A69xPtUYNqM4ix01baOiwF6Bl4jIGLSrrj1S+3P54olxHo0cK+HmpwAuT3DD7KZ+Ml5hJ07O6/f4SRUFzCrJojAzldml3T2sglONXmoTKOMFcNqsIppcXlbujn+Bvf6JJCIyBu2pD252/MiNp7JwUl58ByPHTEE/gVezy0OW0xFZ6fj4F06n3e3jpIr8ATffTk4yPPOVM/scD081HmwObTGVlxhbTJ03r5Qsp4P/bKhmxRCb4MaKMl4iImPQvoZg4DW1IDPOI5FjaVZp99RhZyTj5WV6cfff87zyHJZNKxgw6BpMeKrxQJOL3PQUshJkijotJZkTp+Sxqarl8CfHmAIvEZExaG+Di/yMlEjtjowPiybnRx5HZ7zyMlIHesuw5GWkELBw36pKyhMk2xU2d0I222va8IWm6OMlMUJdEZFxZn+DiymFynaNN4smd7eH6PQEpxYbXR4qijJ58IYVpCQdXb5k+fRCMlKTcXn8TMhJjPqusLkTcnD7Auxt6GBmSfz2blTGS0RkDNpd105FYUa8hyHH2PSiLK49ZSoQzHhZa6ltdVOak8biKfkcP6n/vl1DtWBiLr/84CIA9jW4jna4Y8rcsmCwtaU6vo1UFXiJiIwxzS4PB1u61DR1HEpKMnz/0gUUZaWyam8je+o7cPsClOb036H+SLxrbjHTizP52gVzjtk1x4JZJdn88SNLWDGjMK7j0FSjiMgY8051KwDzFHiNW2kpyWw40MLZv3gJgNJjOC3odCTz/FfPOmbXGytSHUk9+pvFizJeIiJjyI6aNv7xViWgbYLGs3BhfdixzHhJfCnjJSIyhnzotjdp7PBw8rSChGl+mYgaO3o2TS3NVuA1XijjJSIyRrh9fho7PKyYUchdnzo53sOREVSSYCsQxzMFXiIiMfbMOzU8sObA4U88jHAW5JKF5aQk68d3ophTmk1aSnK8hyHHiKYaRURi7NN/Ww1076n4+q4GTpleSFLS8DqPhwOvgkw1TU0U2390ISnJw+9QL6OXAi8RkRiy1kYeVzZ28k51K9fftYYfXjqfa06pGNa1ugMvTTuNd89+5Uzq2tykOpTZHG8UeImIxFBVaDNigLX7myLB0+aDrcO+ljJeiWNmSRYzS7IOf6KMOQq8RERiaEdNe+Tx397YS1FWMFvV0atdwFAo4yUy9inwEhGJoZe215GanMR7TijngbXdBfb7GjqGfa3GDg/GQG66Ml4iY5Umj0VEYsTt8/PQ21WcP7+UL507C0dUMf2Omna8/sCwrtfQ4SE/I5XkYRbli8joocBLRCRGtlS30dLp5aLjy5hckMGa75wXea3T6+f+1Qew1vLAmgPUtnUNei1rLSt3N6juR2SMG3LgZYyZaow5N/Q43RiTHbthiYiMbesqm7n2jlUAnDA5DwhOEf78ihP452dOAeBbD23kS/9Yx1fvX8+vn90x6PVe39XArroOLj9xYkzHLSKxNaTAyxjzaeBfwJ9ChyYB/47RmERExrQOt4/Lfv8aLZ1eAMpzu7d7+cCSSSybVsDXLpgDwMPrDkbeMxC3z88X7n2bqYUZXHJCeQxHLiKxNtSM1+eAU4FWAGvtDqAkVoMSERnLdtR2r2Q8fVYRxvStyfrcu2YyKT898jy67URvGw+00NDh4ZsXHkeWU2uiRMayof4f7LbWesI/PIwxDsAO/hYwxuwF2gA/4LPWLjXGFAD/ACqAvcAHrbVNwx65iMgI+94jm7nrzX3s/PFFg54XznTd/amTOamiYMDzUqO2/dnb4OrxmrWWR9YfZGJeOiv3NAKwbNrA1xKRsWGogddLxphvAenGmPOAG4D/DPG977LW1kc9vwl4zlp7izHmptDzbwx5xCIicfLX1/cC0NrlJSdt4JYO4cCrNMc5aOfxLm+wl9fZc0t4fmstLo+PjFQHXV4/N96zlme31HLCpFyy0hzMKsmiIDP12H0zIhIXQ51qvAmoAzYCnwEeB759hJ95KXBn6PGdwGVHeB0RkREVbuOw7VDboOeFA6+cw/Tb+sCSSQC8e/4EALaHmq3+v2e28+yWWhZMzGH9gRZe29nA+fNLj2rsIjI6DDXwSgfusNZeYa39AHBH6NjhWOBpY8waY8x1oWOl1tpqgNBX1YqJyKgTvcdiWLgma2v14Nv9tIYCr8M1Ov3SubNZ/93zOX12EQBr9zURCFjuXrmfSxeV84WzZ0XOfY+K6kXGhaEGXs/RM9BKB54dwvtOtdYuBi4EPmeMOWOoAzPGXGeMWW2MWV1XVzfUt4mIHDGfP8D9qyt5eXsdC7/3NGv2NfZ4PTy9uHUIGa+0lCScjuRBz0tKMuRmpFCWm055bhp/fnUP7/vD67S7fZw6o4iTKgooy03j+jNnMHdCztF9cyIyKgy1xivNWhtZpmOtbTfGZBzuTdbag6GvtcaYh4BlQI0xpsxaW22MKQNqB3jvbcBtAEuXLj1sIb+IyNF6cvMhvvavDZHnm6paWTK1u6A93PJhX69C+N5aXN5hb+tz5pwS7l21P7K6cf7EHPIzU3n9prP7XRUpImPTUDNeHcaYxeEnxpglwMBrn4PnZIabrBpjMoHzgU3AI8C1odOuBR4e7qBFRGKhyeXt8bzD07O3Vns48GocfJ/F5k7PsAOv7713Hj99/8LI81klwR7VCrpExpehZry+BNxvjDkYel4GfOgw7ykFHgr90HAA91hrnzTGvAX80xjzSWA/cMWwRy0iEgMHmoKZrIzUZFwePy29A7FQ4FXV1InHFxhwxWJL5/AzXk5HMh88aTILJ+eys7Z90NWQIjJ2DSnwsta+ZYyZC8wBDLDVWus9zHt2Ayf0c7wBOOcIxioiElMHGjuZVpTJs185k1N+8hzNUYFXIGDp8PiZmJdOVXMnVc3Bc3u7e+U+3tzdyIKJR1aTNXdCjuq5RMaxQQMvY8zZ1trnjTGX93ppljEGa+2DMRybiMiIqmxyMSk/neQkQ35GKs2dnshrrlDPrXnlOVQ1d7KvoaPfwOuFrcGy1RMn54/MoEVkTDlcxutM4HngPf28ZgEFXiIyblQ2upi/oAyA3IwUdtS2U9/upijLGZlmnFeWwzPv1LC/sf8Ce2MME3LS+O+LjxuxcYvI2DFo4GWtvdkYkwQ8Ya395wiNSURkxLV1eWlyeZlSEFywnZeewqo9jSz90bPs+vFFtHUFpx2nFWWSnpLMztp27lu1n4sWlvXoYt/i8lJRlEFayuCtJEQkMR22xstaGzDG3Ago8BKRcSucwZpaGAy8nFGB0+xvP4E/EOxqk+V0MKUgg7+9sQ+APQ0dfPPC7uxWc6eH6UVZIzVsERljhrps5hljzH8ZYyYbYwrCf2I6MhGREVQZCrzCGa/oqcRw0AWQ6XREtg4CePjtgwSiXm92ecnLGN6KRhFJHEMNvD5BcGPsl4DVUX9ERMaFcKA1ORR4zS4JZq3mlGb3OG9yQTqlOU4APveuGRxq7Yo0PbXW0tzpJVeBl4gMYKh9vOYRDLxOI1hU/wrwx1gNSkRkpO1rcJGXkRLpv/X9S+fz8VOnkZ3m4K+v7yU7zcGSqflMys/gpx84gR01bfgClt+/sIvqli4mF2TQ5Q3g8QXIS0+N83cjIqPVUAOvO4FW4Deh51eFjn0wFoMSERlp+xtdkWlGgIxUB/PKg/20vnPJvB7nFmc7Kc52srM2uJPawVDGK9x+QlONIjKQoQZec6y10c1QXzDGrI/FgERERpK1lltf3MUrO+q5eGHZsN5bnpcGwMGWYOD119f3AsEVkSIi/Rlqjdfbxpjl4SfGmJOB12IzJBGRkVPZ2MnPntoG0CPjNRQZqQ7yM1I42NzJlupW/vTSbgAKMjXVKCL9G2rG62Tgo8aY/aHnU4AtxpiNgLXWLhz4rSIio4/HF+Dr/1rPhNz0yLGpwwy8AMpy0znY3MWfX90DwA8vnc9JFVr0LSL9G2rg9e6YjkJEZIRtPdTKv9cd7HEs/wgyVeV56eysbWPlbjcfWjqZa06pOEYjFJHxaKibZO+L9UBEREbS3obuPl0LJuZQlOVkxYzCYV9nYl4az26pAeC9i8qP2fhEZHwaasZLRGRc2VPXAcBNF87lYysqjniLn/K87qnKxVO0MbaIDE6Bl4gkpD317UzMS+f6M2cc1XWiA6/0VO3PKCKDG+qqRhGRcWVvg4tpRZlHfZ3owEtE5HAUeIlIQmrp9B6Ttg/hXl45aZpAEJHD008KERn3/AHLT5/cSrvbR02rm//76BLaurxkHYNgqTQ7jStPmsyVy6Ycg5GKyHinwEtExrXdde185u9r2BHa3gfgzd2NtHb5yD4GgVdSkuGW96uVoYgMjaYaRWTcqmx08dvnd/YIugD+8dZ+PL4AOWna2kdERpYyXiIybr37Vy/T4fEzryyHCblpPL+1FoAXt9cBHJOMl4jIcOinjoiMWx0ePwDTijLJidq4utnlBRR4icjI01SjiIwLd7y6h2vvWBV5HghYHEmG/IwUvnPJPJyO4I+702cVRc7JcmqqUURGlgIvERkXfvDoO7y0vY7KxuBWQLvr2/EFLF85bzYTctMwJnjenNLsyHuU8RKRkabAS0TGPGtt5PHzW2upbunk3F++DEBpTrDP1gXzJwBw2YkTI+cq8BKRkaafOiIy5lU1d0Yerz/QzPGTciPPy3KDneWXTy9kz08uwoRTX6BVjSIy4pTxEpExb1NVCwDGQHVzF80uT+S1CblpkcfRQRdAllP/9hSRkaXAS0TGvFd21JOZmswF8yZwsKWTxo7gqsXPvWsGxdnOPucfPzGYETsWnetFRIZDP3VEZMx7eUcdp8woYmpRBs9vraWxww3AZ86c0e/5d33yZHbUtpGSrH97isjI0k8dERnTXB4flY2dLJ6ax8S8dDz+ADtq2nEkGbIHmErMzUhhaUXBCI9UREQZLxEZ4+rbgvVcxVlOCjJTAdhY1UJ+Zmqfmi4RkXhTxktExrS69uC0YlGWk0n5GQBsPdRGQUZqPIclItIvBV4iMqY1RAVeUwoyIsfzM9UqQkRGHwVeIjKm1bcHpxoLs1JJT01mQqhhamFW39WMIiLxpsBLRMa0cMarMCs4tRgu6zppan68hiQiMiAFXiIyptW3u8lOc+B0JAPQ7vYBcOackngOS0SkX1rVKCJjzqaqFv6z4SBnzCrmzjf2URQ1rfina5bw9OYaKgozBrmCiEh8KPASkTHno3esorHDQ6fHD8D1Z06PvLZiRhErZhTFa2giIoNS4CUiY0ZtaxeVTZ00dgQL6lfubiQ/I4VPnT79MO8UERkdYh54GWOSgdVAlbX2EmNMAfAPoALYC3zQWtsU63GIyNj31fvX88qO+sjzbTVtzJ2QHccRiYgMz0gU138R2BL1/CbgOWvtLOC50HMRkcNy+wJ9jk3ITYvDSEREjkxMAy9jzCTgYuD2qMOXAneGHt8JXBbLMYjI+JGT1jdJH+7bJSIyFsQ64/Ur4OtA9D9TS6211QChr1rzLSJD0trlY9m0Anb+z4Vcs3wqAP6AjfOoRESGLmaBlzHmEqDWWrvmCN9/nTFmtTFmdV1d3TEenYiMRW1dPnLSHDiSk/jsWTNITjJcdHxZvIclIjJksSyuPxV4rzHmIiANyDHG3AXUGGPKrLXVxpgyoLa/N1trbwNuA1i6dKn+SSsitHV5yU4LFtOX56Wz838uxIRb1YuIjAExy3hZa79prZ1kra0ArgSet9Z+BHgEuDZ02rXAw7Eag4iML21dPrKj6rwUdInIWBOPLYNuAc4zxuwAzgs9FxEZlLWWdnfPwEtEZKwZkZ9g1toXgRdDjxuAc0bic0Vk/HB5/PgDluy0lHgPRUTkiGmTbBEZE9q6gptfK+MlImOZAi8RGRN21LYBKOMlImOaAi8RGfWaXR6u+fMqQBkvERnbFHiJyKh331uVACQZmF2qvRlFZOzSPx1FZNRbu6+JmSVZPPuVM+M9FBGRo6KMl4iMeo0dHoqznPEehojIUVPgJSKjXqPLQ0FWaryHISJy1BR4icio19ThoSBDgZeIjH0KvERkVPMHLM2dXvIzFXiJyNinwEtERrVmlwdroSBD/btEZOxT4CUio1qTywOgjJeIjAtqJyEio5LPH2B/o4vGDi8ABQq8RGQcUOAlIqPOM+/UcP1da/AHLD+4dD4A+SquF5FxQFONIjLq3PTABvwBC8DPn9pGltPBzJKsOI9KROToKfASkVHFH7C0dHq5dFE5AK1dPi4+voy0lOQ4j0xE5Ogp8BKRUaWmtQtfwLJsWgFpKcEfUZ89a0acRyUicmyoxktERpWq5k4AJual88BnV+DxBagoyozzqEREjg0FXiIyqry2sx6ASfnpzCzJjvNoRESOLU01isiosbO2jV89uwOAiXkZcR6NiMixp8BLREaNysbgNONVyyaTnqpiehEZfxR4icio0dgR7FL/mTNUTC8i45MCLxEZNbQ9kIiMdwq8RGTUaOzwkJxkyEnTuh8RGZ8UeInIqNHk8pCfkYoxJt5DERGJCQVeIjJqNHZ4KMhMifcwRERiRoGXiIwaTR1ebYYtIuOaCilEJG4qG100dHhIMrBwUh6NLg+ztBm2iIxjCrxEJC7+/XYVX/7nOqwFY+DnHziBfQ0dvGtOcbyHJiISM5pqFJER1+728d2HN1FRmMlnz5qBtfDV+9eT5XTw2bNmxnt4IiIxo4yXiIy4h9dV0drl42+fPJlFk/PYUdPOs1tqOPe4UgrUw0tExjFlvETkmPH5A1hrD3ve1uo2ctNTWDQ5D4DpxZkALAw9FxEZr5TxEokjay3tbh+3vbybkyoKOGP22K1vem1nPV+4923mT8zltmuWkJYy8F6Lexs6mFrYvQn2DWfNwAAfWDxpBEYqIhI/CrxEYuzxjdWkJieR6kjqEVh5fAGu+OPrrD/QAkCW08Gm718wYuP65+pKXt1RT3KS4QeXzic7bfj9szy+AK/sqGNLdSs/f3o7AC9vr+Oelfv5xGnTBnzfvgYXJ0Rlt/IyUvnmRccN+/NFRMYaBV4iMbS/wcUNd6+NPF/7nfMiNUz/WX8wEnQBlOelHdPPrmx0UZabhiO5b0WBtZafPrmN+nY3AO8cbOVP1yyhoihzyNd/c3cDP3z0HTYfbAUgycDdn1rOLU9u5a6V+7h6+RScjr5ZL48vwIEmF5cuKj/C70xEZOxSjZdIDN331n4A0lKC/6st/uEzvLy9jic3HeKr969nckE6/7r+FAD8gcPXRh1OuL7qvx/ayOk/fYF/rj7Q47XNB1vYVdfO4xsPUd/u5oeXLeCcuSVsq2njc/esxeMLDOlzvP4A33xwI5sPtvKJU6ex8lvnsPF7F3DKjEJuOGsGu+s6+O+HNvX73u01bQQsVBQOPcgTERkvlPESiZE1+5q4Z9V+zpxdzF8/fhIX/eZVtlS38tE7VgEwKT+dez61nMkFGXxsRQUPrDlwmCv27/mtNTy9uYZDrV28sqOejNRk2rp8AGw40MyVJ02mqrmTmx7cwGs7G3q8d8WMQt6/eCL3rNzPjx7bwnn/7yV+ccUJLK0o6Pezurx+6tvdnPa/LwDw9XfP4YZe7R8umD+B68+cwR9f2sVVy6awZGp+5LVOj59fPbudjNRkzp5bckTfr4jIWKbASyQGXtlRxzV/DgZYFx9fhjGGf39uBa2dPv79dhWPbqzma+fPYXJBsMC8JMdJm9tHh9tHpnN4/1t+4d51tLt9kec5acHVgs0uL/e9VclTmw/R5PKSmZrMgok5bKpq5edXnEB6SjIzioNd4j91+nRmlmTx7X9v4r/uX89DN5zK39/cx0XHl5GbnsKvn9tOs8vL4xurWTGjKPJZp8/sfzHAjWfP5D/rD/K5u9fyoZMm88auBn774RP5xdPbeHZLLV+7YA75ahshIglIgZdIDPzhxV0AnHtcKe8+fgIATkcyxdnJfPqM6Xz6jOk9zi/NDtZ31ba5mTZA4HWwuZPtNW10uP1cvLAMCNZLuX1+AL510VxmlWTzrlAm6aYHNrCxqoWU5CQ+ffo0rl1RQVluOo0dHoqznX2uf9acEr598XFcf9daTvzhMwDc+uJOurzB6cckAwELr+4MFuRfs3wq88pz+h1rltPB7dcu5WN/WcWvn9sBwPce2czT79Tw8VMr+Ny71CRVRBKTAi8Zt6y11La5Kc05tkXrQ7HtUBtXnjSZW96/cEjnh8f45u4GpvVT4N7a5eXi37xCk8sLQFHWck6eXsjmgy14/ZZbr17MRceX9XhPXmiz6W+8ey7vX9LdpqG/oCvsgvkT+L+PLmXboVaKspw8vukQL2+v48QpeTx0w6k8uPYAd76xj+9eMq/HFGJ/jivL4bmvnsXuunZufWEXT2w6BMBnz5wxhDsiIjI+xSzwMsakAS8DztDn/Mtae7MxpgD4B1AB7AU+aK1titU4JDG1u31c8+eVvL2/mQc+ewpLpvZfsxQLjR0eGjo8zBzGZs9LpuazcFIu33xwI00uT6RuqqXTy6o9jby8vY4ml5dPnDqNv7+5lw/d9iY/ft/xbK9pIyXZcPK0vt/f9WdOZ2J+OpedOHHI4zDGcN68Us6bVwrAFUsn83+v7Obc44LPL188icuH0Wsry+lg4aQ8fnjZArYcauXSRRMpiUMgLCIyWpihdJk+ogsbY4BMa227MSYFeBX4InA50GitvcUYcxOQb639xmDXWrp0qV29enVMxinjT2Wji0//bTVbD7UBwRqr31+9OPK6tZbgf57D5/EFqGru7DcrBcHVfit3N/KRP6/krx8/ibPmDL2AvMvr57Lfv4Yxhie+eDq1bV184A9vsL/RFfw+Fpbx+w8vZtuhNr7+wAbWVzYD8N4TyvnNVSce0fczkgIBS1LSkd13EZGxxBizxlq7tL/XYpbxssGIrj30NCX0xwKXAmeFjt8JvAgMGniJDMetL+5ie00bf7pmCav2NPK3N/by8LoqHt9YzeIp+fzfK3v49ZWLOHVm0eEv1svNj2zm3lX7Oa4sB2st+xpcLJtWQFVzJynJSVQ1uWgNrSicVZo9rGunpQRX+t328m66vH7++tpeqpo7+cLZM3ljdwNfO38OAHMmZHPXJ5fx8LqDtHR6x0w/LAVdIiIxzHgBGGOSgTXATOD31tpvGGOarbV5Uec0WWsHLRZRxkt6s9ayp76D6cU9p/M2VbVw5W1vcv78Un75wUVsqmrhkt++2uf904szef6rZ0WeDzUbU3HTY5HH+RkpdHj8eHwBJuSkMas0i6IsJ1MLM5iUn8EHlgx/+5vHN1Zzw91rMQashXfNKeYvH1827OuIiEj8xCXjBWCt9QOLjDF5wEPGmAVDfa8x5jrgOoApU6bEZoAyZt328m5+8sRWHv38aSyYmBs5/qV/rCMjNZkbzgoWcM8vz2FGcSa76jooyXbS7vaxYGIuq/Y0UtnoYnJBBv/vme3cv7qSJ798Bjm9ts1ZX9nML57Zzukzi2jo8PR4be13zmNLdRvffXgTt7x/4bBqugayeEo+KcmG8rx0ynPTuV6F6CIi40pMM149PsiYm4EO4NPAWdbaamNMGfCitXbOYO9Vxkue2nyIO17dw52fWMZtL+/ml88E9wX82gVzIq0J9tZ3cNbPX+Tm98zj46d27xP4m+d28MtntnPze+bxkeVT2d/o4pxfvERGajKP3Hga5/7yJQBOnJLHrVcvZkJOGu1uH05HMuf+8qVIjRUE66xufNdMnI6kPtm2Y6Xd7SMzNfmI69BERCS+4pLxMsYUA15rbbMxJh04F/hf4BHgWuCW0NeHYzWG4fL4AqQ6tIvSaHTD3WvxByzffXhTj21wnt1Sw8XHl7FqbyMrdzcC9OmIfsXSSby0vY5z5paSkpzEjOIsvv/e+fzw0XciQdcZs4tZs7eR9/z2VYqynGyraaMw00l9u5s7PraUr92/gYYOD9+++DjKctNj+r1mDbOBqoiIjB2xXNW4kGDxfDLBPSH/aa39gTGmEPgnMAXYD1xhrW0c7FojkfH68eNbuO3l3fziihN69DyS0WH5j5/jUGsXACdMyuXBG07l72/s5Xv/eafHeVefPIX/ed/xQ7rmM+/UsHJ3A3kZKXzq9Om8tbeRb/97E/saghmu8+eV8r4TJ3Lh8WXUtnaxp76Dk6cXHttvTERExp14rWrcAPRZ426tbQDOidXnHgmXx8ff3tgLwOu7GhR4jULJUYXv58+fQHKS4SPLp7J6XxP7G118/NQKphdlsXBS7iBX6Sm6XxXA6bOKeepLZ/DnV/dw2YkTmZjXndkqyUlT/ykRETlqmtMAXtpWR5c3OM24am8DtW1dlGTrl+xo4fUHqG7ppDw3jQ8smcQnTwvWbzmSk/jdhxcf5t3Dk5aSrO1sREQkZlTQFLKsooCrT55CZWMnF/26b/sBiZ+qpk4CFr583my+cv4c0lKS4z0kERGRI6LAC7jw+DL+ef0pkaLs+nZ3nEck0bYeagVgxjFo1yAiIhJPCryinD6rmM+eNYMkE2yoKaPD+gMtpCQb5pXlxHsoIiIiR0WBVy9FWU4CFlq7vPEeSsKoa3Pz1t6BF7aur2xm7oQcTTGKiMiYp8Crl4LMYOfyJpcCr2PN6w/wq2e3c9nvX6PF5aW6pROAy//wGlf88Q0OtXT1ec9rO+tZtaeRpRWD7iolIiIyJmhVYy95GakANHZ4mFaUOeKf//sXdlLb2sX3Lx3y7koxcaili5ZOL3MmDG+j5/74/AF+9vQ2HttQzYGmYLB1wg+eBuCDSydR2Rg89uPHt/Djy4+PNBB9ZP1BvnDv26SnJPOZM7R1joiIjH0KvHrJDwVezS7PYc6MjZ89tQ2Am98zf0ibNsfK+259jeqWLnb8z4WkJA8/MRq96fSrO+v500u7OW1mEd+5ZB4Pra3iyc2HAPjn6gPMLs3ijFnF3P7qHh7bWM2KGYWcP6+U3z6/k7LcNO67bjkTctXeQ0RExj4FXr0URGW8RlpbVF3Z5oOtHD+MZqAA1lr++9+beGtPIxNy0/juJfOYVXpkGavq0LTf757fydTCDC5fPPSmsk9uquZz97zN/PIcrj2lgp117SQnGW776BIyUh2cPbeEF7bWUpabzso9DVx0fBnleelcvLCMB9dW8e+3q3hlRz1TCjK49erFTC0c+cyjiIhILIzYJtlHYyQ3yW7t8rLwe8FpsMe/cDrzykdmJd3K3Q28tL2OW1/cFTl296dOZsnU/CEVlTe0u7niT2+wu64jciw1OYn/umA21w1zmq7D7WP+zU/1OHbVsskUZzn58nmzMcbQ4vLy73VVTC3MYMHEXIqynOyoaeN3L+zkuS21uH1+ynLTIxtML5yUyyM3njakz+/y+qlp7WJSfkaPjvUiIiJjQVy2DBqrsqM2KL7oN6/w4/cdz4dPnhLzz/3w7SvxByypjiQ8vgAAV9++kssXTyQnLYXati7a3X4m56dH9iLcUdPGZ+9eS4fbR0lOGrvrOvjyubO58eyZ1LZ18b1HNvPjx7ey4UALqY4kvnr+nB7b4EAwS9bQ4cEfsBRnOUlKMmyvaQPgWxfNpaXTyz/eOsA/Vx/AH7D86eXdVBRm0tDhifQ7y0xN5uEbT+XmRzbz+q4GjIF/Xb+CxVPy+PnT27hn5X7ed+LEId+LtJRkZblERGRcUsarH6/sqKPD7eP6u9ayfHoB9113Skw/z+XxMe+7T+F0JPGfz5/G1MIMlv/4uQFXVt5//SmcVFHAR25fyYYDzbh9Ady+QJ8Nov0By3cf3sQDaw/Q5Q3w1fNm8/lzZrF2fxNPbjrEuceVcssTW1i7vxmAFTMK+fkVJ3Drizu5e+V+Xr/pbMpy0wkELF0+P/O+G8yCXTC/FEdSEufOK8HpSObGe9YyMT+dysZO3nfiRK4/c8YxKcoXEREZiwbLeCnwGsQ3H9zI4xur+dWVi8hyOjipouCor1nV3MkvntrGDy9bQGYou7ZmXxPv/8Pr/N9Hl0Y2bX7nYCs3PbiBDQdaAFg2rYCfvn8hl//hdU6eVsCSqfn86LEtfOW82Vy4YAIvbqvj46dW4OinEN5ayyW/fZUsp4N/fOYULvntK2yqao28/qGlkynMSu0xzXnx8WX8/uqe+yC+sauB5CTDsmk978P7//A6a/Y1sXRqPnd96mT12xIRkYSmqcYjNK8sm3tX7efjf3kLgL23XHzU17z9ld08+HYVK/c0kpGazB0fO4n1lc3Bz4uqJ5tXnsOPLlvAe3/3GgA/uHQ+FUWZXH7iRG5/dQ9PbDrErJIsrj55CoVZzkGL6I0xnDaziDte28NrO+vZVNXK58+eyaGWLura3fzk8uNJSjKcNquIHTXteP0BLjq+rM91TplR2O/1v3nhXB5ed5BvXDhXQZeIiMggFHgNYl758FYVDkVqKCNV1RzsXXXrizt55p1ajp+YS3mvlgkLJ+WxfHoBb+5uZGZxcJ/Cj51awe2v7gHg3uuWU5jlHNLnXrVsCn9+dQ9X376SlGTDB5ZM6lNHtWJGEStmFA37e1paUcDSY5ANFBERGe8UeA1i8ZQ8Prh0Ev9cfQAIrnjMSUsZ0nv/+toeHny7ioc/dyrGdK/MOxhq0xAOwO5dVUmW08GPLlvQ47ywuz+1nGaXJzKFOCk/g3s/vZy6djdFQwy6ACqKMvl/H1rEO9WtXLKwTMXrIiIicaAtgwZhjOGnHziBP35kCQB76zsO845u3/vPO2w40MLO2vYexw80uVhWUcC6m8+jODsYON35iZM4YXJev9dJTjJ9slqnzCjkvSeUD+M7CXrPCeV8491zmR+DTJ6IiIgcngKvIZheHMwO9Q6iBpOdFkwm3vnGXqIXMFQ1dVJRlEFGqoNbr17Mty8+jiVTNU0nIiKSCBR4DcH0okyKspz86tkd/P6FnVSGmoIOtK1QIGBxh3px3fXmfl7b2QDAzto2atvcTCnIAOCEyXl86vTpI/AdiIiIyGigwGsIHMlJvO/EcvY3uvjZU9u4a+U+Vu9tZNEPnuHRDQdx+/wAbD7YwmW/f407XtuDxxfgC2fPBGBvQ3CK8pYntpGfkcIHl06O2/ciIiIi8aPi+iG68exZzCrJ5qdPbWNfvYu1+5uCx+95m2XTCvjnZ07h8Y3VrKtsZl2oPcTiqfkYA7VtwQ7vW6pbOWtOCSU52vBZREQkESnwGqLc9BQ+eNJkntp8iL0NHZRHbb2zak8jS3/0LPXtbuaUZnPR8WW4vD6WTy+kKMtJbWsXHW4fVc2dzCjWakIREZFEpcBrmKYWZvL6rgaKstoix0pzulcdTinM4Ivnzoo8L8l2UtvmZk9oReSMUD8uERERSTwKvIapoiiDTq+fV3fWs2JGIZ89awanzyqmod3NR+9YxdW9NtQuzUmjprWLbYeCgdqMEgVeIiIiiUqB1zCdPK2QiXnp5Kan8N33zGPuhOA2P4VZTh77wul9zs9ITWbzwVa+ev96JualM61IU40iIiKJSoHXMM2ZkM1rN5095POPn5jLoxuqyUxN5geXzieln02sRUREJDEo8IqxT542jfcvmTSs7X1ERERkfFL6JcYcyUkKukRERARQ4CUiIiIyYhR4iYiIiIwQBV4iIiIiI0SBl4iIiMgIUeAlIiIiMkIUeImIiIiMEAVeIiIiIiNEgZeIiIjICFHgJSIiIjJCFHiJiIiIjBBjrY33GA7LGFMH7IvxxxQB9TH+jLFI96Uv3ZP+6b70pXvSP92XvnRP+jdW78tUa21xfy+MicBrJBhjVltrl8Z7HKON7ktfuif9033pS/ekf7ovfeme9G883hdNNYqIiIiMEAVeIiIiIiNEgVe32+I9gFFK96Uv3ZP+6b70pXvSP92XvnRP+jfu7otqvERERERGiDJeIiIiIiNEgRdgjHm3MWabMWanMeameI9npBhj7jDG1BpjNkUdKzDGPGOM2RH6mh/12jdD92ibMeaC+Iw6towxk40xLxhjthhjNhtjvhg6nuj3Jc0Ys8oYsz50X74fOp7Q9wXAGJNsjHnbGPNo6LnuiTF7jTEbjTHrjDGrQ8cS+r4YY/KMMf8yxmwN/Xw5RffEzAn9NxL+02qM+dK4vy/W2oT+AyQDu4DpQCqwHpgX73GN0Pd+BrAY2BR17KfATaHHNwH/G3o8L3RvnMC00D1Ljvf3EIN7UgYsDj3OBraHvvdEvy8GyAo9TgFWAssT/b6EvtevAPcAj4ae657AXqCo17GEvi/AncCnQo9TgbxEvye97k8ycAiYOt7vizJesAzYaa3dba31APcBl8Z5TCPCWvsy0Njr8KUEf0AQ+npZ1PH7rLVua+0eYCfBezeuWGurrbVrQ4/bgC3ARHRfrLW2PfQ0JfTHkuD3xRgzCbgYuD3qcELfk0Ek7H0xxuQQ/IfunwGstR5rbTMJfE/6cQ6wy1q7j3F+XxR4BX+pVkY9PxA6lqhKrbXVEAxCgJLQ8YS7T8aYCuBEgtmdhL8voSm1dUAt8Iy1VvcFfgV8HQhEHUv0ewLBoPxpY8waY8x1oWOJfF+mA3XAX0LT0rcbYzJJ7HvS25XAvaHH4/q+KPAKTqH0pqWefSXUfTLGZAEPAF+y1rYOdmo/x8blfbHW+q21i4BJwDJjzIJBTh/398UYcwlQa61dM9S39HNsXN2TKKdaaxcDFwKfM8acMci5iXBfHATLOv5grT0R6CA4hTaQRLgnEcaYVOC9wP2HO7WfY2PuvijwCkbMk6OeTwIOxmkso0GNMaYMIPS1NnQ8Ye6TMSaFYNB1t7X2wdDhhL8vYaEpkheBd5PY9+VU4L3GmL0ESxTONsbcRWLfEwCstQdDX2uBhwhOByXyfTkAHAhliQH+RTAQS+R7Eu1CYK21tib0fFzfFwVe8BYwyxgzLRR1Xwk8EucxxdMjwLWhx9cCD0cdv9IY4zTGTANmAaviML6YMsYYgnUYW6y1v4x6KdHvS7ExJi/0OB04F9hKAt8Xa+03rbWTrLUVBH9uPG+t/QgJfE8AjDGZxpjs8GPgfGATCXxfrLWHgEpjzJzQoXOAd0jge9LLVXRPM8J4vy/xru4fDX+AiwiuXtsF/He8xzOC3/e9QDXgJfgviU8ChcBzwI7Q14Ko8/87dI+2ARfGe/wxuienEUxdbwDWhf5cpPvCQuDt0H3ZBHw3dDyh70vU93oW3asaE/qeEKxnWh/6szn8M1X3hUXA6tD/Q/8G8hP9noS+zwygAciNOjau74s614uIiIiMEE01ioiIiIwQBV4iIiIiI0SBl4iIiMgIUeAlIiIiMkIUeImIiIiMEAVeIjKuGGPyjDE3hB6XG2P+Fe8xiYiEqZ2EiIwroT02H7XWDralkYhIXDjiPQARkWPsFmBGaEPvHcBx1toFxpiPAZcBycAC4BdAKnAN4AYustY2GmNmAL8HigEX8Glr7daR/iZEZHzSVKOIjDc3AbtscEPvr/V6bQHwYYJ7B/4P4LLBTYvfAD4aOuc24PPW2iXAfwG3jsSgRSQxKOMlIonkBWttG9BmjGkB/hM6vhFYaIzJAlYA9we37QTAOfLDFJHxSoGXiCQSd9TjQNTzAMGfh0lAcyhbJiJyzGmqUUTGmzYg+0jeaK1tBfYYY64AMEEnHMvBiUhiU+AlIuOKtbYBeM0Yswn42RFc4mrgk8aY9cBm4NJjOT4RSWxqJyEiIiIyQpTxEhERERkhCrxERERERogCLxEREZERosBLREREZIQo8BIREREZIQq8REREREaIAi8RERGREaLAS0RERGSE/H/dlP4wROQttwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制曲线\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(10,5))\n",
    "plt.plot(price)\n",
    "plt.title(\"close price\")\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#define X y\n",
    "#define method to extract X y\n",
    "# 提取数据队列\n",
    "def extract_data(data,time_step):\n",
    "    X = []\n",
    "    y = []\n",
    "    #0,1,2,3....9 :10个样本 time_step=8; 0-7,1-8,2-9 三组\n",
    "    for i in range(len(data) - time_step ):\n",
    "        X.append([a for a in data[ i: i+time_step ]])\n",
    "        y.append(data[i+time_step])\n",
    "    X = np.array(X)\n",
    "    X = X.reshape(X.shape[0],X.shape[1],1)#维度1\n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(723, 8, 1) [[[0.38327341]\n",
      "  [0.38926621]\n",
      "  [0.38966573]\n",
      "  ...\n",
      "  [0.35637235]\n",
      "  [0.35876948]\n",
      "  [0.35583966]]\n",
      "\n",
      " [[0.38926621]\n",
      "  [0.38966573]\n",
      "  [0.37954455]\n",
      "  ...\n",
      "  [0.35876948]\n",
      "  [0.35583966]\n",
      "  [0.35583966]]\n",
      "\n",
      " [[0.38966573]\n",
      "  [0.37954455]\n",
      "  [0.3818085 ]\n",
      "  ...\n",
      "  [0.35583966]\n",
      "  [0.35583966]\n",
      "  [0.34531895]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.80023971]\n",
      "  [0.79131709]\n",
      "  [0.7857238 ]\n",
      "  ...\n",
      "  [0.75229724]\n",
      "  [0.75109868]\n",
      "  [0.75056599]]\n",
      "\n",
      " [[0.79131709]\n",
      "  [0.7857238 ]\n",
      "  [0.77600213]\n",
      "  ...\n",
      "  [0.75109868]\n",
      "  [0.75056599]\n",
      "  [0.7384472 ]]\n",
      "\n",
      " [[0.7857238 ]\n",
      "  [0.77600213]\n",
      "  [0.7610867 ]\n",
      "  ...\n",
      "  [0.75056599]\n",
      "  [0.7384472 ]\n",
      "  [0.73312026]]]\n"
     ]
    }
   ],
   "source": [
    "time_step = 8\n",
    "X,y = extract_data(price_norm,time_step)\n",
    "print(X.shape,X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "> (723, 8, 1) \n",
    ">\n",
    ">723个样本 每个样本8个数据点 每个数据点1个数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.38327341]\n",
      " [0.38926621]\n",
      " [0.38966573]\n",
      " [0.37954455]\n",
      " [0.3818085 ]\n",
      " [0.35637235]\n",
      " [0.35876948]\n",
      " [0.35583966]]\n",
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0.8423225462777999, 0.844320149154348, 0.8505793048341989, 0.8290051937674789, 0.82780663204155, 0.8162205353575709, 0.8095618591024104, 0.8170195765081901, 0.8158210147822612, 0.8246104674390731, 0.8150219736316421, 0.8208816087361832, 0.8408576375016646, 0.8395259022506325, 0.8310027966440271, 0.814356106006126, 0.814356106006126, 0.8034358769476627, 0.8033027034225596, 0.8071647356505526, 0.8170195765081901, 0.8002397123451858, 0.7913170861632707, 0.7857237981089359, 0.7760021307764017, 0.7610866959648421, 0.7522972433080304, 0.7510986815821015, 0.7505659874816886, 0.7384471966972966, 0.7331202556931681, 0.7224663736849114]\n"
     ]
    }
   ],
   "source": [
    "print(X[0,:,:])#第一个样本\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn_1 (SimpleRNN)     (None, 5)                 35        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 41\n",
      "Trainable params: 41\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 建立模型\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,SimpleRNN\n",
    "model = Sequential()\n",
    "#input_shape 训练长度 每个数据的维度\n",
    "model.add(SimpleRNN(units=5,input_shape=(time_step,1),activation=\"relu\"))\n",
    "#输出层\n",
    "#输出数值 units =1 1个神经元 \"linear\"线性模型\n",
    "model.add(Dense(units=1, activation=\"linear\"))\n",
    "#配置模型 回归模型y\n",
    "model.compile(optimizer=\"adam\",loss=\"mean_squared_error\")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "723/723 [==============================] - 1s 1ms/step - loss: 0.7963\n",
      "Epoch 2/200\n",
      "723/723 [==============================] - 0s 137us/step - loss: 0.0338\n",
      "Epoch 3/200\n",
      "723/723 [==============================] - 0s 129us/step - loss: 0.0064\n",
      "Epoch 4/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 0.0037\n",
      "Epoch 5/200\n",
      "723/723 [==============================] - 0s 130us/step - loss: 0.0034\n",
      "Epoch 6/200\n",
      "723/723 [==============================] - 0s 134us/step - loss: 0.0033\n",
      "Epoch 7/200\n",
      "723/723 [==============================] - 0s 146us/step - loss: 0.0032\n",
      "Epoch 8/200\n",
      "723/723 [==============================] - 0s 156us/step - loss: 0.0031\n",
      "Epoch 9/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 0.0030\n",
      "Epoch 10/200\n",
      "723/723 [==============================] - 0s 134us/step - loss: 0.0029\n",
      "Epoch 11/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 0.0029\n",
      "Epoch 12/200\n",
      "723/723 [==============================] - 0s 137us/step - loss: 0.0028\n",
      "Epoch 13/200\n",
      "723/723 [==============================] - 0s 126us/step - loss: 0.0027\n",
      "Epoch 14/200\n",
      "723/723 [==============================] - 0s 179us/step - loss: 0.0026\n",
      "Epoch 15/200\n",
      "723/723 [==============================] - 0s 149us/step - loss: 0.0026\n",
      "Epoch 16/200\n",
      "723/723 [==============================] - 0s 126us/step - loss: 0.0025\n",
      "Epoch 17/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 0.0025\n",
      "Epoch 18/200\n",
      "723/723 [==============================] - 0s 140us/step - loss: 0.0024\n",
      "Epoch 19/200\n",
      "723/723 [==============================] - 0s 158us/step - loss: 0.0024\n",
      "Epoch 20/200\n",
      "723/723 [==============================] - 0s 180us/step - loss: 0.0023\n",
      "Epoch 21/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 0.0024\n",
      "Epoch 22/200\n",
      "723/723 [==============================] - 0s 129us/step - loss: 0.0023\n",
      "Epoch 23/200\n",
      "723/723 [==============================] - 0s 134us/step - loss: 0.0023\n",
      "Epoch 24/200\n",
      "723/723 [==============================] - 0s 153us/step - loss: 0.0022\n",
      "Epoch 25/200\n",
      "723/723 [==============================] - 0s 187us/step - loss: 0.0022\n",
      "Epoch 26/200\n",
      "723/723 [==============================] - 0s 149us/step - loss: 0.0022\n",
      "Epoch 27/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 0.0022\n",
      "Epoch 28/200\n",
      "723/723 [==============================] - 0s 184us/step - loss: 0.0022\n",
      "Epoch 29/200\n",
      "723/723 [==============================] - 0s 174us/step - loss: 0.0022\n",
      "Epoch 30/200\n",
      "723/723 [==============================] - 0s 173us/step - loss: 0.0022\n",
      "Epoch 31/200\n",
      "723/723 [==============================] - 0s 148us/step - loss: 0.0021\n",
      "Epoch 32/200\n",
      "723/723 [==============================] - 0s 129us/step - loss: 0.0021\n",
      "Epoch 33/200\n",
      "723/723 [==============================] - 0s 134us/step - loss: 0.0021\n",
      "Epoch 34/200\n",
      "723/723 [==============================] - 0s 148us/step - loss: 0.0021\n",
      "Epoch 35/200\n",
      "723/723 [==============================] - 0s 138us/step - loss: 0.0021\n",
      "Epoch 36/200\n",
      "723/723 [==============================] - 0s 134us/step - loss: 0.0021\n",
      "Epoch 37/200\n",
      "723/723 [==============================] - 0s 140us/step - loss: 0.0021\n",
      "Epoch 38/200\n",
      "723/723 [==============================] - 0s 162us/step - loss: 0.0021\n",
      "Epoch 39/200\n",
      "723/723 [==============================] - 0s 139us/step - loss: 0.0021\n",
      "Epoch 40/200\n",
      "723/723 [==============================] - 0s 143us/step - loss: 0.0021\n",
      "Epoch 41/200\n",
      "723/723 [==============================] - 0s 212us/step - loss: 0.0020\n",
      "Epoch 42/200\n",
      "723/723 [==============================] - 0s 172us/step - loss: 0.0020\n",
      "Epoch 43/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 0.0020\n",
      "Epoch 44/200\n",
      "723/723 [==============================] - 0s 169us/step - loss: 0.0021\n",
      "Epoch 45/200\n",
      "723/723 [==============================] - 0s 170us/step - loss: 0.0020\n",
      "Epoch 46/200\n",
      "723/723 [==============================] - 0s 198us/step - loss: 0.0020\n",
      "Epoch 47/200\n",
      "723/723 [==============================] - 0s 167us/step - loss: 0.0020\n",
      "Epoch 48/200\n",
      "723/723 [==============================] - 0s 142us/step - loss: 0.0020\n",
      "Epoch 49/200\n",
      "723/723 [==============================] - 0s 138us/step - loss: 0.0019\n",
      "Epoch 50/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 0.0021\n",
      "Epoch 51/200\n",
      "723/723 [==============================] - 0s 174us/step - loss: 0.0020\n",
      "Epoch 52/200\n",
      "723/723 [==============================] - 0s 206us/step - loss: 0.0020\n",
      "Epoch 53/200\n",
      "723/723 [==============================] - 0s 259us/step - loss: 0.0020\n",
      "Epoch 54/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 0.0019\n",
      "Epoch 55/200\n",
      "723/723 [==============================] - 0s 205us/step - loss: 0.0020\n",
      "Epoch 56/200\n",
      "723/723 [==============================] - 0s 209us/step - loss: 0.0019\n",
      "Epoch 57/200\n",
      "723/723 [==============================] - 0s 154us/step - loss: 0.0019\n",
      "Epoch 58/200\n",
      "723/723 [==============================] - 0s 127us/step - loss: 0.0019\n",
      "Epoch 59/200\n",
      "723/723 [==============================] - 0s 124us/step - loss: 0.0019\n",
      "Epoch 60/200\n",
      "723/723 [==============================] - 0s 149us/step - loss: 0.0018\n",
      "Epoch 61/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 0.0019\n",
      "Epoch 62/200\n",
      "723/723 [==============================] - 0s 124us/step - loss: 0.0018\n",
      "Epoch 63/200\n",
      "723/723 [==============================] - 0s 166us/step - loss: 0.0019\n",
      "Epoch 64/200\n",
      "723/723 [==============================] - 0s 136us/step - loss: 0.0019\n",
      "Epoch 65/200\n",
      "723/723 [==============================] - 0s 136us/step - loss: 0.0018\n",
      "Epoch 66/200\n",
      "723/723 [==============================] - 0s 166us/step - loss: 0.0018\n",
      "Epoch 67/200\n",
      "723/723 [==============================] - 0s 113us/step - loss: 0.0018\n",
      "Epoch 68/200\n",
      "723/723 [==============================] - 0s 140us/step - loss: 0.0018\n",
      "Epoch 69/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 0.0018\n",
      "Epoch 70/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 0.0018\n",
      "Epoch 71/200\n",
      "723/723 [==============================] - 0s 227us/step - loss: 0.0017\n",
      "Epoch 72/200\n",
      "723/723 [==============================] - 0s 210us/step - loss: 0.0019\n",
      "Epoch 73/200\n",
      "723/723 [==============================] - 0s 141us/step - loss: 0.0017\n",
      "Epoch 74/200\n",
      "723/723 [==============================] - 0s 123us/step - loss: 0.0017\n",
      "Epoch 75/200\n",
      "723/723 [==============================] - 0s 173us/step - loss: 0.0017\n",
      "Epoch 76/200\n",
      "723/723 [==============================] - 0s 140us/step - loss: 0.0017\n",
      "Epoch 77/200\n",
      "723/723 [==============================] - 0s 127us/step - loss: 0.0017\n",
      "Epoch 78/200\n",
      "723/723 [==============================] - 0s 162us/step - loss: 0.0017\n",
      "Epoch 79/200\n",
      "723/723 [==============================] - 0s 222us/step - loss: 0.0016\n",
      "Epoch 80/200\n",
      "723/723 [==============================] - 0s 185us/step - loss: 0.0016\n",
      "Epoch 81/200\n",
      "723/723 [==============================] - 0s 156us/step - loss: 0.0016\n",
      "Epoch 82/200\n",
      "723/723 [==============================] - 0s 156us/step - loss: 0.0016\n",
      "Epoch 83/200\n",
      "723/723 [==============================] - 0s 176us/step - loss: 0.0016\n",
      "Epoch 84/200\n",
      "723/723 [==============================] - 0s 216us/step - loss: 0.0016\n",
      "Epoch 85/200\n",
      "723/723 [==============================] - 0s 198us/step - loss: 0.0017\n",
      "Epoch 86/200\n",
      "723/723 [==============================] - 0s 172us/step - loss: 0.0017\n",
      "Epoch 87/200\n",
      "723/723 [==============================] - 0s 219us/step - loss: 0.0016\n",
      "Epoch 88/200\n",
      "723/723 [==============================] - 0s 198us/step - loss: 0.0017\n",
      "Epoch 89/200\n",
      "723/723 [==============================] - 0s 201us/step - loss: 0.0015\n",
      "Epoch 90/200\n",
      "723/723 [==============================] - 0s 185us/step - loss: 0.0016\n",
      "Epoch 91/200\n",
      "723/723 [==============================] - 0s 212us/step - loss: 0.0016\n",
      "Epoch 92/200\n",
      "723/723 [==============================] - 0s 144us/step - loss: 0.0016\n",
      "Epoch 93/200\n",
      "723/723 [==============================] - 0s 142us/step - loss: 0.0015\n",
      "Epoch 94/200\n",
      "723/723 [==============================] - 0s 136us/step - loss: 0.0015\n",
      "Epoch 95/200\n",
      "723/723 [==============================] - 0s 148us/step - loss: 0.0015\n",
      "Epoch 96/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 0.0015\n",
      "Epoch 97/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "723/723 [==============================] - 0s 160us/step - loss: 0.0016\n",
      "Epoch 98/200\n",
      "723/723 [==============================] - 0s 140us/step - loss: 0.0016\n",
      "Epoch 99/200\n",
      "723/723 [==============================] - 0s 116us/step - loss: 0.0015\n",
      "Epoch 100/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 0.0014\n",
      "Epoch 101/200\n",
      "723/723 [==============================] - 0s 138us/step - loss: 0.0014\n",
      "Epoch 102/200\n",
      "723/723 [==============================] - 0s 152us/step - loss: 0.0014\n",
      "Epoch 103/200\n",
      "723/723 [==============================] - 0s 166us/step - loss: 0.0014\n",
      "Epoch 104/200\n",
      "723/723 [==============================] - 0s 129us/step - loss: 0.0014\n",
      "Epoch 105/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 0.0014\n",
      "Epoch 106/200\n",
      "723/723 [==============================] - 0s 128us/step - loss: 0.0014\n",
      "Epoch 107/200\n",
      "723/723 [==============================] - 0s 130us/step - loss: 0.0014\n",
      "Epoch 108/200\n",
      "723/723 [==============================] - 0s 127us/step - loss: 0.0014\n",
      "Epoch 109/200\n",
      "723/723 [==============================] - 0s 144us/step - loss: 0.0013\n",
      "Epoch 110/200\n",
      "723/723 [==============================] - 0s 143us/step - loss: 0.0013\n",
      "Epoch 111/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 0.0013\n",
      "Epoch 112/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 0.0013\n",
      "Epoch 113/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 0.0013ETA: 0s - loss: 0.0013   \n",
      "Epoch 114/200\n",
      "723/723 [==============================] - 0s 165us/step - loss: 0.0013\n",
      "Epoch 115/200\n",
      "723/723 [==============================] - 0s 137us/step - loss: 0.0013\n",
      "Epoch 116/200\n",
      "723/723 [==============================] - 0s 137us/step - loss: 0.0012\n",
      "Epoch 117/200\n",
      "723/723 [==============================] - 0s 174us/step - loss: 0.0013\n",
      "Epoch 118/200\n",
      "723/723 [==============================] - 0s 214us/step - loss: 0.0012\n",
      "Epoch 119/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 0.0012\n",
      "Epoch 120/200\n",
      "723/723 [==============================] - 0s 159us/step - loss: 0.0012\n",
      "Epoch 121/200\n",
      "723/723 [==============================] - 0s 160us/step - loss: 0.0012\n",
      "Epoch 122/200\n",
      "723/723 [==============================] - 0s 180us/step - loss: 0.0012\n",
      "Epoch 123/200\n",
      "723/723 [==============================] - 0s 166us/step - loss: 0.0011\n",
      "Epoch 124/200\n",
      "723/723 [==============================] - 0s 165us/step - loss: 0.0011\n",
      "Epoch 125/200\n",
      "723/723 [==============================] - 0s 160us/step - loss: 0.0012\n",
      "Epoch 126/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 0.0011\n",
      "Epoch 127/200\n",
      "723/723 [==============================] - 0s 130us/step - loss: 0.0011\n",
      "Epoch 128/200\n",
      "723/723 [==============================] - 0s 126us/step - loss: 0.0011\n",
      "Epoch 129/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 0.0011\n",
      "Epoch 130/200\n",
      "723/723 [==============================] - 0s 207us/step - loss: 0.0011\n",
      "Epoch 131/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 0.0012\n",
      "Epoch 132/200\n",
      "723/723 [==============================] - 0s 172us/step - loss: 0.0011\n",
      "Epoch 133/200\n",
      "723/723 [==============================] - 0s 205us/step - loss: 0.0011\n",
      "Epoch 134/200\n",
      "723/723 [==============================] - 0s 177us/step - loss: 0.0011\n",
      "Epoch 135/200\n",
      "723/723 [==============================] - 0s 159us/step - loss: 0.0011ETA: 0s - loss: 0.001\n",
      "Epoch 136/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 0.0010\n",
      "Epoch 137/200\n",
      "723/723 [==============================] - 0s 130us/step - loss: 0.0010\n",
      "Epoch 138/200\n",
      "723/723 [==============================] - 0s 127us/step - loss: 0.0011\n",
      "Epoch 139/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 0.0010\n",
      "Epoch 140/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 0.0011\n",
      "Epoch 141/200\n",
      "723/723 [==============================] - 0s 126us/step - loss: 0.0010\n",
      "Epoch 142/200\n",
      "723/723 [==============================] - 0s 126us/step - loss: 9.9545e-04\n",
      "Epoch 143/200\n",
      "723/723 [==============================] - 0s 178us/step - loss: 0.0010 0s - loss: 9.2841e-\n",
      "Epoch 144/200\n",
      "723/723 [==============================] - 0s 151us/step - loss: 0.0010\n",
      "Epoch 145/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 9.9100e-04\n",
      "Epoch 146/200\n",
      "723/723 [==============================] - 0s 123us/step - loss: 9.6066e-04\n",
      "Epoch 147/200\n",
      "723/723 [==============================] - 0s 130us/step - loss: 9.8308e-04\n",
      "Epoch 148/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 0.0010\n",
      "Epoch 149/200\n",
      "723/723 [==============================] - 0s 160us/step - loss: 0.0010\n",
      "Epoch 150/200\n",
      "723/723 [==============================] - 0s 191us/step - loss: 9.3493e-04\n",
      "Epoch 151/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 9.9187e-04\n",
      "Epoch 152/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 9.4913e-04\n",
      "Epoch 153/200\n",
      "723/723 [==============================] - 0s 134us/step - loss: 9.4141e-04\n",
      "Epoch 154/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 9.4563e-04\n",
      "Epoch 155/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 9.3766e-04\n",
      "Epoch 156/200\n",
      "723/723 [==============================] - 0s 144us/step - loss: 9.8135e-04\n",
      "Epoch 157/200\n",
      "723/723 [==============================] - 0s 207us/step - loss: 9.1599e-04\n",
      "Epoch 158/200\n",
      "723/723 [==============================] - 0s 123us/step - loss: 9.3188e-04\n",
      "Epoch 159/200\n",
      "723/723 [==============================] - 0s 136us/step - loss: 8.9076e-04\n",
      "Epoch 160/200\n",
      "723/723 [==============================] - 0s 126us/step - loss: 8.7989e-04\n",
      "Epoch 161/200\n",
      "723/723 [==============================] - 0s 120us/step - loss: 0.0010\n",
      "Epoch 162/200\n",
      "723/723 [==============================] - 0s 138us/step - loss: 9.0978e-04\n",
      "Epoch 163/200\n",
      "723/723 [==============================] - 0s 152us/step - loss: 8.7300e-04\n",
      "Epoch 164/200\n",
      "723/723 [==============================] - 0s 176us/step - loss: 8.6328e-04\n",
      "Epoch 165/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 8.7409e-04\n",
      "Epoch 166/200\n",
      "723/723 [==============================] - 0s 163us/step - loss: 8.7894e-04\n",
      "Epoch 167/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 8.6414e-04\n",
      "Epoch 168/200\n",
      "723/723 [==============================] - 0s 147us/step - loss: 8.6981e-04\n",
      "Epoch 169/200\n",
      "723/723 [==============================] - 0s 127us/step - loss: 9.1786e-04\n",
      "Epoch 170/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 8.1247e-04\n",
      "Epoch 171/200\n",
      "723/723 [==============================] - 0s 180us/step - loss: 8.0478e-04\n",
      "Epoch 172/200\n",
      "723/723 [==============================] - 0s 149us/step - loss: 9.1365e-04\n",
      "Epoch 173/200\n",
      "723/723 [==============================] - 0s 129us/step - loss: 8.4761e-04\n",
      "Epoch 174/200\n",
      "723/723 [==============================] - 0s 137us/step - loss: 8.2362e-04\n",
      "Epoch 175/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 8.2070e-04\n",
      "Epoch 176/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 7.9685e-04\n",
      "Epoch 177/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 7.7599e-04\n",
      "Epoch 178/200\n",
      "723/723 [==============================] - 0s 148us/step - loss: 7.6817e-04\n",
      "Epoch 179/200\n",
      "723/723 [==============================] - 0s 123us/step - loss: 7.8376e-04\n",
      "Epoch 180/200\n",
      "723/723 [==============================] - 0s 119us/step - loss: 7.7524e-04\n",
      "Epoch 181/200\n",
      "723/723 [==============================] - 0s 160us/step - loss: 7.7517e-04\n",
      "Epoch 182/200\n",
      "723/723 [==============================] - 0s 162us/step - loss: 7.6781e-04\n",
      "Epoch 183/200\n",
      "723/723 [==============================] - 0s 183us/step - loss: 7.6344e-04\n",
      "Epoch 184/200\n",
      "723/723 [==============================] - 0s 170us/step - loss: 7.9186e-04\n",
      "Epoch 185/200\n",
      "723/723 [==============================] - 0s 176us/step - loss: 8.2922e-04\n",
      "Epoch 186/200\n",
      "723/723 [==============================] - 0s 141us/step - loss: 8.4157e-04\n",
      "Epoch 187/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 8.5283e-04\n",
      "Epoch 188/200\n",
      "723/723 [==============================] - 0s 137us/step - loss: 7.4873e-04\n",
      "Epoch 189/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "723/723 [==============================] - 0s 152us/step - loss: 7.3023e-04\n",
      "Epoch 190/200\n",
      "723/723 [==============================] - 0s 115us/step - loss: 7.3549e-04\n",
      "Epoch 191/200\n",
      "723/723 [==============================] - 0s 120us/step - loss: 7.1474e-04\n",
      "Epoch 192/200\n",
      "723/723 [==============================] - 0s 131us/step - loss: 7.3582e-04\n",
      "Epoch 193/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 7.4116e-04\n",
      "Epoch 194/200\n",
      "723/723 [==============================] - 0s 122us/step - loss: 9.1111e-04\n",
      "Epoch 195/200\n",
      "723/723 [==============================] - 0s 118us/step - loss: 7.3464e-04\n",
      "Epoch 196/200\n",
      "723/723 [==============================] - 0s 119us/step - loss: 7.1025e-04\n",
      "Epoch 197/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 7.4810e-04\n",
      "Epoch 198/200\n",
      "723/723 [==============================] - 0s 133us/step - loss: 7.1318e-04\n",
      "Epoch 199/200\n",
      "723/723 [==============================] - 0s 124us/step - loss: 6.8360e-04\n",
      "Epoch 200/200\n",
      "723/723 [==============================] - 0s 145us/step - loss: 6.7477e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x220d6508588>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练\n",
    "model.fit(X,y,batch_size=30,epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#预测 训练数据\n",
    "y_train_predict = model.predict(X) * max(price)\n",
    "y_train = [i*max(price) for i in y]#归一化数据转换回来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(10,5))\n",
    "plt.plot(y_train,label = \"real price\")\n",
    "plt.plot(y_train_predict,label = \"predict price\")\n",
    "plt.title(\"price\")\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>price_change</th>\n",
       "      <th>p_change</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma20</th>\n",
       "      <th>v_ma5</th>\n",
       "      <th>v_ma10</th>\n",
       "      <th>v_ma20</th>\n",
       "      <th>turnover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-24</th>\n",
       "      <td>78.88</td>\n",
       "      <td>78.89</td>\n",
       "      <td>77.00</td>\n",
       "      <td>76.38</td>\n",
       "      <td>1072693.38</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>79.898</td>\n",
       "      <td>79.961</td>\n",
       "      <td>79.539</td>\n",
       "      <td>1002925.93</td>\n",
       "      <td>1029867.65</td>\n",
       "      <td>1261360.37</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>79.00</td>\n",
       "      <td>79.88</td>\n",
       "      <td>79.00</td>\n",
       "      <td>78.02</td>\n",
       "      <td>782100.44</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>80.102</td>\n",
       "      <td>80.495</td>\n",
       "      <td>79.302</td>\n",
       "      <td>963394.30</td>\n",
       "      <td>1069260.28</td>\n",
       "      <td>1237982.51</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-22</th>\n",
       "      <td>80.21</td>\n",
       "      <td>81.85</td>\n",
       "      <td>79.93</td>\n",
       "      <td>79.44</td>\n",
       "      <td>877513.25</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>79.946</td>\n",
       "      <td>81.187</td>\n",
       "      <td>78.950</td>\n",
       "      <td>1070608.21</td>\n",
       "      <td>1133509.96</td>\n",
       "      <td>1237859.29</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21</th>\n",
       "      <td>82.95</td>\n",
       "      <td>83.20</td>\n",
       "      <td>80.76</td>\n",
       "      <td>80.37</td>\n",
       "      <td>904774.81</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>80.004</td>\n",
       "      <td>81.763</td>\n",
       "      <td>78.592</td>\n",
       "      <td>1079659.15</td>\n",
       "      <td>1215397.62</td>\n",
       "      <td>1227575.20</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>79.05</td>\n",
       "      <td>82.82</td>\n",
       "      <td>82.80</td>\n",
       "      <td>79.05</td>\n",
       "      <td>1377547.75</td>\n",
       "      <td>4.78</td>\n",
       "      <td>6.13</td>\n",
       "      <td>80.112</td>\n",
       "      <td>82.009</td>\n",
       "      <td>78.225</td>\n",
       "      <td>1096415.78</td>\n",
       "      <td>1347124.29</td>\n",
       "      <td>1214135.95</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high  close    low      volume  price_change  p_change  \\\n",
       "date                                                                         \n",
       "2020-07-24  78.88  78.89  77.00  76.38  1072693.38         -2.00     -2.53   \n",
       "2020-07-23  79.00  79.88  79.00  78.02   782100.44         -0.93     -1.16   \n",
       "2020-07-22  80.21  81.85  79.93  79.44   877513.25         -0.83     -1.03   \n",
       "2020-07-21  82.95  83.20  80.76  80.37   904774.81         -2.04     -2.46   \n",
       "2020-07-20  79.05  82.82  82.80  79.05  1377547.75          4.78      6.13   \n",
       "\n",
       "               ma5    ma10    ma20       v_ma5      v_ma10      v_ma20  \\\n",
       "date                                                                     \n",
       "2020-07-24  79.898  79.961  79.539  1002925.93  1029867.65  1261360.37   \n",
       "2020-07-23  80.102  80.495  79.302   963394.30  1069260.28  1237982.51   \n",
       "2020-07-22  79.946  81.187  78.950  1070608.21  1133509.96  1237859.29   \n",
       "2020-07-21  80.004  81.763  78.592  1079659.15  1215397.62  1227575.20   \n",
       "2020-07-20  80.112  82.009  78.225  1096415.78  1347124.29  1214135.95   \n",
       "\n",
       "            turnover  \n",
       "date                  \n",
       "2020-07-24      0.99  \n",
       "2020-07-23      0.72  \n",
       "2020-07-22      0.81  \n",
       "2020-07-21      0.84  \n",
       "2020-07-20      1.27  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成数据\n",
    "import tushare as ts \n",
    "# 读取中国平安（601318）数据\n",
    "zgpa = ts.get_hist_data('601318', start='2020-01-01', end='2020-07-24')\n",
    "# 查看数据前5行\n",
    "zgpa.head()\n",
    "# 输出数据\n",
    "#zgpa.to_csv('zgpa_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-07-24    77.00\n",
       "2020-07-23    79.00\n",
       "2020-07-22    79.93\n",
       "2020-07-21    80.76\n",
       "2020-07-20    82.80\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测 测试数据\n",
    "data_test = zgpa\n",
    "price_test = data_test.loc[:,'close']\n",
    "price_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(127, 8, 1) 127\n"
     ]
    }
   ],
   "source": [
    "price_test_norm = price_test/max(price)\n",
    "X_test_norm,y_test_norm = extract_data(price_test_norm,time_step)\n",
    "print(X_test_norm.shape,len(y_test_norm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_test_predict = model.predict(X_test_norm) * max(price)\n",
    "y_test = [i*max(price) for i in y_test_norm]#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(10,5))\n",
    "plt.plot(y_test,label = \"real price test\")\n",
    "plt.plot(y_test_predict,label = \"predict price test\")\n",
    "plt.title(\"price\")\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(127, 1) (127, 1)\n",
      "(127, 2)\n"
     ]
    }
   ],
   "source": [
    "result_y_test = np.array(y_test).reshape(-1,1) #若干行,一列\n",
    "result_y_test_predict = y_test_predict\n",
    "print(result_y_test.shape,result_y_test_predict.shape)\n",
    "result = np.concatenate((result_y_test,result_y_test_predict),axis=1)#合并结果\n",
    "print(result.shape)\n",
    "reslut = pd.DataFrame(result,columns=['real_predict_test','predict_price_test'])\n",
    "reslut.to_csv(\"zgpa_predict.csv\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "预测结果有延时"
   ]
  }
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